Functions and Focus of Supervisory Feedback on Undergraduate Students’ Theses Writing
Bibliographic record
Abstract
In higher English education, undergraduate students improve in their academic writing through the feedback of their supervisors. Supervisor feedback can enhance students’ English writing, However, the functions and focus of feedback remain underexplored. This study examines the functions and focus of supervisory feedback on undergraduate students’ thesis writing across three drafts. The study adopts a descriptive research design to analyze supervisor feedback (369 comments) on undergraduate writing drafts among students enrolled in a Global Communication program at a Malaysian university. Fifteen thesis drafts submitted by five students (three drafts per student) were analyzed to identify the functions and focus of supervisory feedback during the academic writing process. The findings reveal that in terms of speech functions, the feedback can be categorized into three main types: directive, referential, and expressive. Directive feedback, which constitutes the largest proportion (56.6%), is primarily used to give explicit instructions for revision. Referential feedback, the second most frequent type (29.8%), provides information or corrections to support improvement. Expressive feedback, although less common (13.6%), serves to offer emotional support and encouragement. Feedback focus covers three key aspects of English proposal writing: content-related issues, organization, and editing appropriateness. Supervisors primarily focus on content, followed by editing appropriateness and organization. The study shows that the scaffolding through comments on successive drafts of thesis enabled undergraduate students to learn academic writing conventions in writing a research proposal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".